INET Presentation

Ernesto Carrella

November 26, 2015

Fisheries today

  • Worm et al(2003): fishless seas by 2050
  • About 30% of the world fisheries have collapsed
  • The need for policy

Code example

    @Override
    public PerTripIterativeDestinationStrategy apply(FishState state) {

        MersenneTwisterFast random = state.random;
        NauticalMap map = state.getMap();


        final DefaultBeamHillClimbing algorithm = new DefaultBeamHillClimbing(
                alwaysCopyBest,
                dynamicFriendshipNetwork,
                stepSize.apply(random).intValue(),10);
        return new PerTripIterativeDestinationStrategy(
                new FavoriteDestinationStrategy(map, random), algorithm,
                probability.apply(state));
        }

    

A brief history in failure

  • Failures of the Commons and Gordon, 1954
  • Effort control and capital stuffing
  • Gear regulations and efficiency drops
  • Area closures and fishing the lines
  • Quotas and its allocation

Objectives

  • Policy Simulator
    • Agents Flexibility
    • Model Flexibility
  • Policy Optimization

The State of the Art

  • Random Utility Models
    • Statistically Efficient
    • Easily Generalizable
    • Policy-Brittle
  • Dynamic Programming
    • Strongly Rational
    • Computationally Expensive
    • Ad hoc

The One Agent Problem

  • Find the most profitable spot to fish
  • Constraints:
    • No biomass information
    • Environment changes over time
  • Subproblems:
    • How to explore
    • Explore-Exploit Tradeoff

One Agent world

One Agent sample run

Fast and Frugal Adaptation

  • How to explore
    • Tow at a nearby cell from subjective best
    • Random Hill-Climbing
  • When to explore
    • Stochastically choose to explore next trip with probability \(p\)
    • Adjust \(p\) if exploration is often (un)succesful
  • Why not more nuanced bandit algorithms?
  • Why not interview grounded algorithms?

Multiple Agents

  • Other boats consume biomass
  • You can use other boats information
  • How to imitate?
  • With probability \(p\) explore, with probability \(i\) ask a friend otherwise exploit

Two Agents sample run

Many Agents

Imitation-Exploration Tradeoff

Imitation-Exploration Tradeoff

Oil Prices

What information they have and they act on

  • Associate a choice with profits (per trip or per month)
  • Compare and imitate profits
  • Proceed by feedback and trial and error

Fish the Line (part 1)

Fish the Line (part 2)

Fish the Rocky Line (part 1)

Fish the Rocky Line (part 2)

Not Just Locational Choices

The Quotas Debate

  • TAC: Total Allowable Catch
  • ITQ: Individual Tradeable Quota

Single Species TAC vs ITQ

Single Species TAC vs ITQ (mileage)

Seventy-Thirty World

ITQ Prices

  • Quotas are distributed 90% reds, 10% blue

Blues are choke species

ITQ drives Gear (start)

ITQ drives Gear (end)

Gear fixes wastes

North-South world

Location choices

ITQ incentivates geography

OSMOSE

Optimization

Optimization (part 2)

What’s next

  • Package this data-free version
  • Move towards more realism
  • More visible testing